A Survey on Artifacts Detection Techniques for Electro-Encephalography (EEG) Signals

Roy, Vandana; Shukla, Shailja

doi:10.14257/ijmue.2015.10.3.39

Cited by 14 publications

(5 citation statements)

References 44 publications

(45 reference statements)

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“…PCA is a multiple data analysis procedure that involves the transformation of many correlated variables into a smaller number of uncorrelated variables. PCA is defined as "linear projection that transforms multivariate data into a set of components called principle components (PCs)" [38]. PCs are calculated using a simple algorithm called singular value decomposition (SVD).…”

Section: Principle Component Analysis (Pca)mentioning

confidence: 99%

Artifact Removal Methods in EEG Recordings: A Review

Mathe

Padmaja

Krishna

2021

Proc. eng. technol. innov.

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To obtain the correct analysis of electroencephalogram (EEG) signals, non-physiological and physiological artifacts should be removed from EEG signals. This study aims to give an overview on the existing methodology for removing physiological artifacts, e.g., ocular, cardiac, and muscle artifacts. The datasets, simulation platforms, and performance measures of artifact removal methods in previous related research are summarized. The advantages and disadvantages of each technique are discussed, including regression method, filtering method, blind source separation (BSS), wavelet transform (WT), empirical mode decomposition (EMD), singular spectrum analysis (SSA), and independent vector analysis (IVA). Also, the applications of hybrid approaches are presented, including discrete wavelet transform - adaptive filtering method (DWT-AFM), DWT-BSS, EMD-BSS, singular spectrum analysis - adaptive noise canceler (SSA-ANC), SSA-BSS, and EMD-IVA. Finally, a comparative analysis for these existing methods is provided based on their performance and merits. The result shows that hybrid methods can remove the artifacts more effectively than individual methods.

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Section: Principle Component Analysis (Pca)mentioning

confidence: 99%

Artifact Removal Methods in EEG Recordings: A Review

Mathe

Padmaja

Krishna

2021

Proc. eng. technol. innov.

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“…Among them, EEG is one of the strongest biomedical signals that has significance and a high practical value for applications in clinical neurology [57]. EEG is a noninvasive method for measuring the electrical activity of the cerebral cortex [73][74][75][76]. EEG has been widely used since its discovery by Berger in 1929 [77].…”

Section: Electroencephalogrammentioning

confidence: 99%

“…As shown in Figure 3 [84], the black circles indicate positions in the 10/20 system; and gray circles indicate extended positions in the 10/10 placement system. measuring the electrical activity of the cerebral cortex [73][74][75][76]. EEG has been widely used since its discovery by Berger in 1929 [77].…”

Section: Electroencephalogrammentioning

confidence: 99%

Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

Aboalayon

Faezipour

Almuhammadi

et al. 2016

Entropy

293

124

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Sleep specialists often conduct manual sleep stage scoring by visually inspecting the patient's neurophysiological signals collected at sleep labs. This is, generally, a very difficult, tedious and time-consuming task. The limitations of manual sleep stage scoring have escalated the demand for developing Automatic Sleep Stage Classification (ASSC) systems. Sleep stage classification refers to identifying the various stages of sleep and is a critical step in an effort to assist physicians in the diagnosis and treatment of related sleep disorders. The aim of this paper is to survey the progress and challenges in various existing Electroencephalogram (EEG) signal-based methods used for sleep stage identification at each phase; including pre-processing, feature extraction and classification; in an attempt to find the research gaps and possibly introduce a reasonable solution. Many of the prior and current related studies use multiple EEG channels, and are based on 30 s or 20 s epoch lengths which affect the feasibility and speed of ASSC for real-time applications. Thus, in this paper, we also present a novel and efficient technique that can be implemented in an embedded hardware device to identify sleep stages using new statistical features applied to 10 s epochs of single-channel EEG signals. In this study, the PhysioNet Sleep European Data Format (EDF) Database was used. The proposed methodology achieves an average classification sensitivity, specificity and accuracy of 89.06%, 98.61% and 93.13%, respectively, when the decision tree classifier is applied. Finally, our new method is compared with those in recently published studies, which reiterates the high classification accuracy performance.

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“…For extracting features and characterization, various one-dimensional (1D) methods have been proposed [ 10 ]. Previous studies have focused on spectral analysis of the EEG signal [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Single Channel EEG Artifact Identification Using Two-Dimensional Multi-Resolution Analysis

Taherisadr

Dehzangi

Parsaei

2017

Sensors

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As a diagnostic monitoring approach, electroencephalogram (EEG) signals can be decoded by signal processing methodologies for various health monitoring purposes. However, EEG recordings are contaminated by other interferences, particularly facial and ocular artifacts generated by the user. This is specifically an issue during continuous EEG recording sessions, and is therefore a key step in using EEG signals for either physiological monitoring and diagnosis or brain–computer interface to identify such artifacts from useful EEG components. In this study, we aim to design a new generic framework in order to process and characterize EEG recording as a multi-component and non-stationary signal with the aim of localizing and identifying its component (e.g., artifact). In the proposed method, we gather three complementary algorithms together to enhance the efficiency of the system. Algorithms include time–frequency (TF) analysis and representation, two-dimensional multi-resolution analysis (2D MRA), and feature extraction and classification. Then, a combination of spectro-temporal and geometric features are extracted by combining key instantaneous TF space descriptors, which enables the system to characterize the non-stationarities in the EEG dynamics. We fit a curvelet transform (as a MRA method) to 2D TF representation of EEG segments to decompose the given space to various levels of resolution. Such a decomposition efficiently improves the analysis of the TF spaces with different characteristics (e.g., resolution). Our experimental results demonstrate that the combination of expansion to TF space, analysis using MRA, and extracting a set of suitable features and applying a proper predictive model is effective in enhancing the EEG artifact identification performance. We also compare the performance of the designed system with another common EEG signal processing technique—namely, 1D wavelet transform. Our experimental results reveal that the proposed method outperforms 1D wavelet.

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A Survey on Artifacts Detection Techniques for Electro-Encephalography (EEG) Signals

Cited by 14 publications

References 44 publications

Artifact Removal Methods in EEG Recordings: A Review

Artifact Removal Methods in EEG Recordings: A Review

Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

Single Channel EEG Artifact Identification Using Two-Dimensional Multi-Resolution Analysis

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